Introduction

The Signal Processing Basics Training Course offers a comprehensive introduction to the fundamental concepts and techniques of signal processing, a critical field in modern engineering and technology. Signal processing is used to analyze, manipulate, and synthesize signals, including audio, video, and sensor data, for a variety of applications in communication systems, audio/video processing, biomedical engineering, and more. This course will cover core signal processing principles, such as filtering, Fourier analysis, and digital signal processing (DSP), while also addressing the latest trends and technologies shaping the future of signal processing.

Objectives

By the end of this course, participants will:

1. Understand the basic concepts and types of signals: continuous vs. discrete, periodic vs. aperiodic.
2. Be familiar with time-domain and frequency-domain signal analysis techniques.
3. Learn about common signal processing tools, such as Fourier Transform, filters, and convolution.
4. Gain practical experience with digital signal processing (DSP) techniques and algorithms.
5. Understand the principles of sampling, quantization, and digital representation of signals.
6. Learn about modern applications of signal processing in communications, image processing, audio processing, and biomedical systems.
7. Be able to design and implement basic filters and perform signal processing operations using simulation tools (e.g., MATLAB).
8. Explore emerging trends in signal processing, such as machine learning in signal analysis.

Who Should Attend?

This course is ideal for:

• Electrical Engineers who need to understand the fundamentals of signal processing for their work in communications, audio/video, and control systems.
• Signal Processing Engineers working in research and development, telecommunications, and other technical fields.
• Students pursuing degrees in electrical engineering, computer science, or related fields who want to gain foundational knowledge in signal processing.
• Technicians and Operators involved in the application of signal processing systems and looking to improve their skills.
• Professionals in fields like biomedical engineering, robotics, and aerospace who need to apply signal processing techniques in their work.

Course Outline

Day 1: Introduction to Signals and Signal Representation

• Session 1: What is a Signal?

  • Definition and types of signals: Continuous vs. discrete, deterministic vs. random.
  • Periodic and aperiodic signals: Examples and properties.
  • Basic signal types: Sinusoids, square waves, impulse signals, and random noise.

• Session 2: Signal Representation in Time Domain

  • Time-domain representation: Amplitude, frequency, and phase of signals.
  • Signal operations: Shifting, scaling, and inversion.
  • Signal transformations: Time-domain to frequency-domain and vice versa.

• Session 3: Fourier Analysis

  • Introduction to Fourier Series and Fourier Transform.
  • Frequency representation of signals: Continuous and discrete Fourier transforms (CFT and DFT).
  • Understanding the concept of frequency, harmonics, and bandwidth.

• Hands-On Workshop: Use MATLAB to analyze signals in the time and frequency domains using Fourier Transform and Fourier Series.

Day 2: Time-Domain and Frequency-Domain Analysis

• Session 1: Convolution and Correlation

  • Convolution in signal processing: Linear time-invariant systems.
  • The role of convolution in filtering and system response.
  • Cross-correlation and autocorrelation: Signal similarity and pattern recognition.

• Session 2: Filters and Frequency Response

  • Basic filter types: Low-pass, high-pass, band-pass, and band-stop filters.
  • Transfer function and frequency response of filters.
  • Filter design: Analog vs. digital filters.

• Session 3: Frequency-Domain Analysis Techniques

  • Power spectral density (PSD) and energy spectral density (ESD).
  • Applications of frequency-domain analysis in communications and signal detection.
  • Spectral analysis tools: Power spectrum, periodogram, and Welch’s method.

• Hands-On Workshop: Implement simple filters (low-pass, high-pass) and analyze signal spectra using MATLAB.

Day 3: Digital Signal Processing (DSP) Techniques

• Session 1: Introduction to Digital Signal Processing

  • Overview of DSP: Digital vs. analog signal processing.
  • The process of sampling: Sampling theorem, aliasing, and Nyquist rate.
  • Quantization: Signal representation in digital form, quantization errors.

• Session 2: Discrete-Time Signals and Systems

  • Discrete signals and their properties: Difference equations, discrete convolution.
  • Z-Transform and its applications in analyzing discrete-time systems.
  • Stability and causality in discrete-time systems.

• Session 3: Digital Filter Design

  • FIR (Finite Impulse Response) vs. IIR (Infinite Impulse Response) filters.
  • Design techniques: Window method, frequency sampling, and bilinear transform.
  • Stability and performance evaluation of digital filters.

• Hands-On Workshop: Design FIR and IIR filters and implement them on sample signals using MATLAB or Python.

Day 4: Advanced Topics in Signal Processing

• Session 1: Multirate Signal Processing

  • Decimation and interpolation: Changing the sampling rate of signals.
  • Applications of multirate processing in audio and communications.
  • Polyphase filters and their use in efficient signal processing.

• Session 2: Wavelet Transform and Time-Frequency Analysis

  • Introduction to wavelets: Continuous vs. discrete wavelet transforms.
  • Applications of wavelet transform in signal compression, denoising, and feature extraction.
  • Time-frequency analysis: STFT (Short-Time Fourier Transform) and Gabor transform.

• Session 3: Signal Processing for Communications

  • Modulation and demodulation techniques: AM, FM, QAM, PSK, etc.
  • Error detection and correction in communication systems.
  • Channel estimation and equalization in wireless communications.

• Hands-On Workshop: Apply wavelet transform and multirate processing techniques to real-world signals such as speech or sensor data.

Day 5: Applications and Future Trends in Signal Processing

• Session 1: Signal Processing in Audio and Image Processing

  • Audio signal processing: Noise reduction, equalization, and speech recognition.
  • Image signal processing: Image compression (JPEG), filtering, and feature extraction.
  • Video signal processing: Motion detection, video compression (H.264).

• Session 2: Biomedical Signal Processing

  • ECG, EEG, and EMG signal analysis and preprocessing.
  • Signal processing for medical imaging (MRI, CT, Ultrasound).
  • Noise reduction, artifact removal, and feature extraction in biomedical signals.

• Session 3: Emerging Trends and Machine Learning in Signal Processing

  • The integration of machine learning in signal processing: Supervised and unsupervised learning.
  • Applications in speech recognition, image classification, and sensor networks.
  • Future trends in signal processing: 5G communications, IoT, and edge computing.

• Hands-On Workshop: Real-time signal processing in audio or biomedical systems and integration with machine learning techniques using MATLAB/Python.

Final Assessment & Certification

• Knowledge Check: A quiz covering the core principles of signal processing, including Fourier analysis, filter design, and DSP techniques.
• Final Project: Design a signal processing system for an application (e.g., audio, biomedical, or communications) and present results.
• Course Completion Certificate: Awarded to participants who successfully complete the course and demonstrate a clear understanding of signal processing fundamentals and their applications.